A measurement of process variables is fundamentally subject to errors. For example, there can be superposed on the measurement signal interfering variables that occur systematically or stochastically, which can influence the actual measurement signal and often which cannot be acquired mathematically. Whereas systematic interfering variables and the measurement errors resulting therefrom can be compensated by suitable measures in the signal flow diagram, stochastic interfering variables and their effects are less capable of being controlled.
Predictive determination of process variables can also be difficult when it is desired to consider a comparatively long time period of, for example, 30 years, and there is a desire for this purpose to evaluate comparatively many measured values approximately continuously or at comparatively short intervals, for example one measured value per minute. Predictive determination involves transfer of the measured data in a functional relationship. Various mathematical methods that are described in the literature exist for this purpose. The method should then be capable of detecting and evaluating long term changes just like short term changes.
By way of example, gastight monitoring in SF6 gas-insulated high-voltage switching installations is such a case. Such gas-insulating switching installations have a technically induced leakage rate that becomes noticeable from a pressure loss, and can be less than 0.5% per year in relation to the desired gas fill pressure when the switching installation is commissioned. In the case of a technical defect, for example a porous seal, however, the leakage rate can be higher.
The recorded measured values for the gas pressure are subject to measurement errors. In particular, the temperature of the insulating gas is a source of such measurement errors. This temperature is dependent on the ambient temperature, which fluctuates in the course of a day and of a year, as well as on the current flow and the heat loss produced thereby in the monitored gas space. It can be likewise difficult to acquire the gas temperature for the purpose of computational correction or compensation of the measurement errors, particularly as induced by the inhomogeneous temperature distribution inside a gas space.
Predictive determination of a continuously measured process variable, specifically the pressure of the insulating gas, should therefore be able to detect and analyze both comparatively slow and comparatively quick variations in the gas pressure. A purely mathematical approximation of all recorded measured values for the gas pressure by means of a mathematical approximation function can be excessively expensive; in particular there would be a relatively high computing power involved.